Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast

• URL: http://arxiv.org/abs/2511.08071v1
• Date: Wed, 12 Nov 2025 01:38:14 GMT
• Title: Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast
• Authors: Ying Wang, Zhaodong Sun, Xu Cheng, Zuxian He, Xiaobai Li,
• Abstract summary: Radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing.<n>Traditional radar-based heartbeat sensing methods face performance degradation due to noise.<n>We propose an unsupervised framework for radar-based heartbeat sensing via Augmented Pseudo-Label and Noise Contrast.
• Score: 20.935264919712658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labeled signals for supervised training. To overcome these limitations, we propose the first unsupervised framework for radar-based heartbeat sensing via Augmented Pseudo-Label and Noise Contrast (Radar-APLANC). We propose to use both the heartbeat range and noise range within the radar range matrix to construct the positive and negative samples, respectively, for improved noise robustness. Our Noise-Contrastive Triplet (NCT) loss only utilizes positive samples, negative samples, and pseudo-label signals generated by the traditional radar method, thereby avoiding dependence on expensive ground-truth physiological signals. We further design a pseudo-label augmentation approach featuring adaptive noise-aware label selection to improve pseudo-label signal quality. Extensive experiments on the Equipleth dataset and our collected radar dataset demonstrate that our unsupervised method achieves performance comparable to state-of-the-art supervised methods. Our code, dataset, and supplementary materials can be accessed from https://github.com/RadarHRSensing/Radar-APLANC.

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